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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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GMD | Articles | Volume 12, issue 2
Geosci. Model Dev., 12, 613–628, 2019
https://doi.org/10.5194/gmd-12-613-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 12, 613–628, 2019
https://doi.org/10.5194/gmd-12-613-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 07 Feb 2019

Methods for assessment of models | 07 Feb 2019

Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets

Grzegorz Muszynski et al.
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Cited articles  
AMS: Atmospheric River, Glossary of Meteorology, available at: http://glossary.ametsoc.org/wiki/Atmosphericriver, last access: 10 January 2018. a
Burges, C. J.: A tutorial on support vector machines for pattern recognition, Data Min. Knowl. Disc., 2, 121–167, 1998. a
Carlsson, G.: Topology and data, B. Am. Math. Soc., 46, 255–308, 2009. a, b, c, d
Carlsson, G.: Topological pattern recognition for point cloud data, Acta Numer., 23, 289–368, 2014. a, b, c, d
Chang, C.-C. and Lin, C.-J.: LIBSVM: a library for support vector machines, ACM T. Intel. Syst. Tec., 2, 27, https://doi.org/10.1145/1961189.1961199, 2011. a, b, c
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We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
We present the automated method for recognizing atmospheric rivers in climate data, i.e.,...
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